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Product Dictionary Learning-Based SAR Target Configuration Recognition

机译：基于产品字典的SAR目标配置识别

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摘要

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.
机译：字典施工是基于稀疏表示的关键因素 - 基于稀疏表示的算法 - （SR-）。已经验证了学习的词典比预定义的字典更有效。在本文中，我们提出了一种产品字典学习（PDL）算法来实现合成孔径雷达（SAR）目标配置识别。所提出的算法从统计观点获取词典，以增强所提出的算法对噪声的鲁棒性。并且，在SAR图像中取得不可避免的乘法斑点，所提出的算法采用产品模型来描述SAR图像。对SAR图像的更准确描述导致较高的识别率。通过移动和静止目标采集和识别（MSTAR）数据库验证了所提出的算法的准确性和鲁棒性。

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